This chart is a little different than the others. People wanted both cases and deaths- it has both. People wanted the chart to start in early March- this one does that. Having a chart that doesn't change as much from day to day (as each day only changes by 1/90th) allows the animation to run smoother and faster. And also, adding everything into one chart makes it easier to keep updated daily. I wanted a single metric that can best characterize the negative effect the virus is having on a population, with only the statistics of cases and deaths to work from. Deaths being most severe, and cases include all kinds of results, from death, to long-term or permanent organ damage, to just being sick for a long time, to no symptoms at all. What I ended up doing was averaging the (per million) daily normalized* deaths and cases for the given 90-day period (but counting deaths three times). The formula is likely to change in the future (and with it, the numbers), but at least for now, I'm using:
** Impact = (CASES + DEATHS * 3) / 4
I hope to add more factors in the future that will give a better representation of the impact. Keep checking back.
What you can take from the chart- When a number is going up, that state is doing worse than they did three months ago (going down means they're doing better). It's a very loose estimate, but you can also divide the number by 10,000 to get a percentage number of the population that at least got really sick from COVID during that three month period. Again, I'm still working on a better formula for a more accurate representation.
* "Normalization" (perhaps better called "smoothing") means the abnormalities in the data were evened out. For example, if there were 10 days in a row of a few cases/deaths a day and then one day of 1000... that looks awful and frenetic on a chart like this, even when framed in a per-week display. In reality, that 1000 is just a backlog catch-up, so I normalized it by spreading the thousand over previous dates for a more even / more realistic data. It works similarly when the total number of cases/deaths drops one day. Likely a correction from a previous report, I just subtracted the difference over previous dates to numbers that are probably closer to reality.